#encoding=utf8
import numpy as np
import pickle
import os
import sys
from tqdm import tqdm

from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.model_selection import ParameterGrid, cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler

def load_dataset(file_name):
    '''
    从文件读入数据集
    被多处调用，请勿删除或改动本函数！！！
    '''
    try:
        with open(file_name, 'rb') as f:
            raw_dataset = pickle.load(f)
    except FileNotFoundError:
        print(f"错误: 文件 {file_name} 未找到。请确保文件路径正确。")
        return None, None
    
    try:
        example_image = raw_dataset[0][0]
    except KeyError:
        print("错误: 数据集格式不正确，无法找到类别0的数据。")
        return None, None
    except TypeError:
        print("错误: 数据集格式不正确，类别0的数据不是列表或数组。")
        return None, None

    dataset = np.empty((0, example_image.size))
    labels = np.empty((0, 1))
    
    total_images = 0

    for i_class in raw_dataset.keys():
        images_list = raw_dataset.get(i_class, [])
        if not isinstance(images_list, list) or len(images_list) == 0:
            continue
        for image in images_list:
            features = image.flatten() / 255.0
            
            dataset = np.vstack((dataset, features))
            labels = np.vstack((labels, i_class))
            
            total_images += 1
            
    print(f"成功加载 {total_images} 张手写数字图片。")

    return dataset, labels

class Classifier:
    def __init__(self):
        self.model = None
        self.pipeline = None

        self.train_dataset, self.train_labels = load_dataset('./step1/input/training_dataset.pkl')
        if self.train_dataset is None:
            raise RuntimeError("训练数据集加载失败，无法继续。")

    def train(self):
        print("=== 正在配置提升树模型并寻找最优参数... ===")

        # Pipeline
        pipeline = Pipeline([
            ('scaler', StandardScaler()), 
            ('gbdt', HistGradientBoostingClassifier(random_state=42))
        ])

        # 超参数网格
        param_grid = {
            'gbdt__max_iter': [100, 200],
            'gbdt__max_depth': [None, 10, 20],
            'gbdt__learning_rate': [0.05, 0.1, 0.2],
            'gbdt__l2_regularization': [0.0, 1.0]
        }

        best_score = -1.0
        best_params = None
        best_model = None

        train_labels = self.train_labels.ravel()
        param_list = list(ParameterGrid(param_grid))

        print(f"共 {len(param_list)} 组参数，每组做 3 折交叉验证，总计 {len(param_list)*3} 次训练")

        with tqdm(total=len(param_list), desc="参数搜索", file=sys.stdout) as pbar:
            for params in param_list:
                # 更新 pipeline 的参数
                pipeline.set_params(**params)

                # 3 折交叉验证
                scores = cross_val_score(
                    pipeline, 
                    self.train_dataset, 
                    train_labels, 
                    cv=3, 
                    scoring='accuracy',
                    n_jobs=-1
                )
                mean_score = np.mean(scores)

                pbar.set_postfix({
                    "当前参数": str(params),
                    "均值准确率": f"{mean_score:.4f}"
                })
                pbar.update(1)

                if mean_score > best_score:
                    best_score = mean_score
                    best_params = params
                    best_model = pipeline

        # 用最佳参数重新拟合全量数据
        best_model.set_params(**best_params)
        best_model.fit(self.train_dataset, train_labels)

        self.model = best_model
        self.pipeline = self.model

        print("=== 模型训练完成 ===")
        print(f"最佳参数: {best_params}")
        print(f"最佳交叉验证准确率: {best_score:.4f}")

    def predict(self, test_dataset):
        predicted_labels = self.pipeline.predict(test_dataset)
        return predicted_labels

def calculate_accuracy(file_name, classifier):
    test_dataset, test_labels = load_dataset(file_name)
    if test_dataset is None:
        return 0
    random_indices = np.random.permutation(test_dataset.shape[0])
    test_dataset = test_dataset[random_indices,:]
    test_labels = test_labels[random_indices,:]
    predicted_labels = classifier.predict(test_dataset)
    if isinstance(predicted_labels, np.ndarray):
        if predicted_labels.size != test_labels.size:
            print('错误：输出的标签数量与测试集大小不一致')
            accuracy = 0
        else:
            accuracy = np.mean(predicted_labels.flatten()==test_labels.flatten())
    else:
        print('错误：输出格式有误，必须为ndarray格式')
        accuracy = 0
    return accuracy

if __name__ == '__main__':
    classifier = Classifier()
    classifier.train()

    sum_accuracies = 0
    num_test_datasets = 0

    test_dir = './step1/input'
    test_files = ['test_dataset_clean.pkl'] + [
        f'test_dataset_noise_type{noise}_level{level}.pkl'
        for noise in range(1, 7)
        for level in range(1, 4)
    ]

    print("\n=== 正在对所有测试集进行评估... ===")
    with tqdm(total=len(test_files), desc="正在测试", file=sys.stdout) as pbar:
        for file_name in test_files:
            file_path = os.path.join(test_dir, file_name)
            pbar.set_description(f"正在测试: {file_name}")
            accuracy = calculate_accuracy(file_path, classifier)
            pbar.set_postfix({'正确率': f'{accuracy:.4f}'})
            pbar.update(1)
            sum_accuracies += accuracy
            num_test_datasets += 1
    
    mean_accuracies = sum_accuracies / num_test_datasets
    print(f'\n你在总共{num_test_datasets}个测试集上的平均正确率为：{mean_accuracies:.4f}')
